In addition to providing printed telephone directories, telephone companies provide information services to their subscribers The services may include stock quotes, directory assistance and many others. In most of these applications, when the information requested can be expressed as a number or number sequence, the user is required to enter his request via a touch tone telephone. This is often aggravating for the user since he is usually obliged to make repetitive entries in order to obtain a single answer. This situation becomes even more difficult when the input information is a word or phrase. In these situations, the involvement of a human operator may be required to complete the desired task.
Because telephone companies are likely to handle a very large number of calls per year, the associated labour costs are very significant Consequently, telephone companies and telephone equipment manufacturers have devoted considerable efforts to the development of systems which reduce the labour costs associated with providing information services on the telephone network These efforts comprise the development of sophisticated speech processing and recognition systems that can be used in the context of telephone networks.
In a typical speech recognition system the user enters his request using isolated word, connected word or continuous speech via a microphone or telephone set. The request may be a name, a city or any other type of information for which either a function is to be performed or information is to be supplied. If valid speech is detected, the speech recognition layer of the system is invoked in an attempt to recognize the unknown utterance. The speech recognition process can be split into two steps namely a pre-processing step and a search step The pre-processing step, also called the acoustic processor, performs the segmentation, the normalisation and the parameterisation of the input signal waveform. Its purpose is traditionally to transform the incoming utterance into a form that facilitates speech recognition. Typically feature vectors are generated at this step. Feature vectors are used to identify speech characteristics such as formant frequencies, fricative, silence, voicing and so on. Therefore, these feature vectors can b used to identify a spoken utterance The second step in the speech recognition process, the search step, includes a speech recognition dictionary that is scored in order to find possible matches to the spoken utterance based on the feature vectors generated in the pre-processing step. The search may be done in several steps in order to maximise the probability of obtaining the correct result in the shortest possible time and most preferably in real-time. Typically, in a first pass search, a fast match algorithm is used to select the top N orthographies from a speech recognition dictionary. In a second pass search the individual orthographies are re-scored using more precise likelihood calculations. The top two orthographies in the re-scored group are then processed by a rejection algorithm that evaluates if they are sufficiently distinctive from one another so that the top choice candidate can be considered to be a valid recognition.
Voiced activated dialling (VAD) systems are often based on speaker trained technology. This allows the user of the service to enter by voice a series of names for which he wishes to use VAD. Each of the names is associated with a phone number that is dialled when the user utters the name. The names and phone number are stored in a "client dictionary" situated in the central repository of the VAD system. Each subscriber of the service has an associated client dictionary. Since the number of subscribers is substantial and the number of entries in each client dictionary can be quite large, the storage requirements for the central repository are very high. Furthermore, each user request requires his respective client dictionary to be downloaded to a temporary storage location in the speech recognition unit, which puts a further load on the system. Compression/Decompression techniques are required to allow the system to support such a load. However prior art techniques that have high compression factors are either not real-time or degrade significantly the performance of the speech recogniser in terms of recognition accuracy of the speech recognition system
Thus, there exists a need in the industry to provide a real-time compression/decompression method such as to minimize the storage requirement of a speech recognition dictionary while maintaining a high recognition accuracy